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Multiple Linear Regression

Linear regression with multiple predictor variables

For greater accuracy on low- through medium-dimensional data sets, fit a linear regression model using fitlm.

For reduced computation time on high-dimensional data sets that fit in the MATLAB® Workspace, fit a linear regression model using fitrlinear.

Classes

LinearModel Linear regression model class
CompactLinearModel Compact linear regression model class
RegressionLinear Linear regression model for high-dimensional data
RegressionPartitionedLinear Cross-validated linear regression model for high-dimensional data

Functions

fitlm Create linear regression model
stepwiselm Create linear regression model using stepwise regression
compact Compact linear regression model
disp Display linear regression model
feval Evaluate linear regression model prediction
predict Predict response of linear regression model
random Simulate responses for linear regression model
plot Scatter plot or added variable plot of linear model
plotAdjustedResponse Adjusted response plot for linear regression model
fitrlinear Fit linear regression model to high-dimensional data
predict Predict response of linear regression model
dummyvar Create dummy variables
invpred Inverse prediction
plsregress Partial least-squares regression
x2fx Convert predictor matrix to design matrix
relieff Importance of attributes (predictors) using ReliefF algorithm
regress Multiple linear regression
robustdemo Interactive robust regression
robustfit Robust regression
rsmdemo Interactive response surface demonstration
rstool Interactive response surface modeling

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